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layer

An easy implementation of convolutional/deconvolutional layer using im_col_convert and gemm API.

For calling the convolutional/deconvolutional layer, you should define yours:

1. input_shape -- 1D tensor of shape [ batch, channel, height, width ]
2. input -- 1D tensor of data stored in format [ batch * channel * height * width ]
3. filter_shape -- 1D tensor of shape [ channel_out, channel_in, height, width ]
4. filter -- 1D tensor of data stored in format [ channel_out * channel_in * height * width ]
5. strides -- 1D tensor of shape [ 1, stride, stride, 1 ]
6. padding -- integer of padding length at each side
7. output_shape -- 1D tensor of shape [ batch, channel, height, width ]
8. output -- 1D tensor of data stored in format [ batch * channel * height * width ]

compile: g++ -std=c++11 gemm.cpp im_col_convert.cpp layer.cpp main.cpp -o main

execution: ./main

test: python test.py

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